Embeddings

interface EmbeddingProvider {
  readonly dimensions: number;
  embed(text: string): Promise<number[]>;
  embedBatch(texts: string[]): Promise<number[][]>;
  /** Optional - true for models like gemini-embedding-2 */
  readonly supportsMultimodal?: boolean;
  /** Optional - returns ONE vector for the full multimodal input */
  embedMultimodal?(input: string | ContentPart | ContentPart[]): Promise<number[]>;
}

npm install openai

import { OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding({
  apiKey: process.env.OPENAI_API_KEY,  // optional, uses env var by default
  model: "text-embedding-3-small",      // optional, this is the default
});

const vector = await embedder.embed("Hello world");
console.log(vector.length); // 1536

const vectors = await embedder.embedBatch(["Hello", "World"]);
console.log(vectors.length); // 2

npm install @google/genai

import { GoogleEmbedding } from "@agentium/core";

const embedder = new GoogleEmbedding({
  apiKey: process.env.GOOGLE_API_KEY,  // optional, uses env var by default
  model: "text-embedding-004",         // optional, this is the default
});

const vector = await embedder.embed("Hello world");
const vectors = await embedder.embedBatch(["Hello", "World"]);

import {
  GoogleEmbedding,
  InMemoryVectorStore,
  partsFromFile,
} from "@agentium/core";

const embedder = new GoogleEmbedding({ model: "gemini-embedding-2" });
const store = new InMemoryVectorStore(embedder);

const imagePart = await partsFromFile("./photo.jpg");

await store.upsert("photos", {
  id: "photo-1",
  content: "Sunset over the Sahara",
  parts: [
    { type: "text", text: "Sunset over the Sahara" },
    imagePart,
  ],
  metadata: { tags: ["nature", "africa"] },
});

const queryImage = await partsFromFile("./query.jpg");
const hits = await store.search("photos", [queryImage], { topK: 5 });
console.log(hits);

const compact = new GoogleEmbedding({
  model: "gemini-embedding-2",
  dimensions: 768,  // also supported: 128-3072; recommended 768 / 1536 / 3072
});

import { KnowledgeBase, InMemoryVectorStore, OpenAIEmbedding } from "@agentium/core";

const embedder = new OpenAIEmbedding();
const vectorStore = new InMemoryVectorStore(1536);

const kb = new KnowledgeBase({
  name: "docs",
  vectorStore,
});